Adaptive Similarity Measure Estimation for Interactive Multimedia Content Retrieval
نویسنده
چکیده
In this paper, we investigate adaptive relevance feedback algorithms for interactive multimedia content personalization. In particular two interesting scenarios are examined. The first uses a weighted cross correlation similarity measure for ranking multimedia data. The second exploits concepts of functional analysis to model the similarity measure as a non-linear function, the type of which is estimated by the users’ preferences. The algorithms are computationally efficient and they can be recursively implemented.
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تاریخ انتشار 2003